In the analysis of observational data, stratifying patients on the estimated propensity scores reduces confounding from measured variables. Confidence intervals for the treatment effect are typically calculated without acknowledging uncertainty in the estimated propensity scores, and intuitively this may yield inferences, which are falsely precise. In this paper, we describe a Bayesian method t...

Background: Previous studies suggested an impaired endothelial function in patients with diabetes. However, the validity of this finding may be limited by the lack of adequate adjustment for further cardiovascular confounders. We assessed endothelial function as measured by flow-mediated dilation (FMD) of the brachial artery in patients with either type 1 or type 2 diabetes in comparison with n...

background: the aim of this study was to investigate the relationship between congenital hypothyroidism and several variables using two different adjustment methods. methods: this matched case-control study was conducted in hamadan province, the west of iran, in 2012 enrolling neonates born between 2005 and 2011 and covered by screening program for congenital hypothyroidism. the neonates with t...

This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averag...

Social discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions’ datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to t...

For some time, propensity score based methods have been frequently applied in the analysis of data from observational studies. The propensity score is the conditional probability of a certain treatment or exposure given patient’s covariates. Propensity score methods are used to eliminate baseline imbalances in covariate distributions between treatment or exposure groups and permit to estimate m...